Ashwin P. Gurnani

Multi-Attribute Decision Making Using Hypothetical Equivalents and Inequivalents

In this paper, the problem of selecting from among a set of alternatives using multiple, potentially conflicting criteria is discussed. A number of approaches are commonly used to make these types of decisions in engineering design, including pairwise comparisons, ranking methods, rating methods, weighted sum approaches, and strength of preference methods. In this paper, we first demonstrate the theoretical and practical flaws with a number of these commonly employed methods. We demonstrate the strengths and weaknesses of the various decision-making approaches using an aircraft selection problem. We then present a method based on the concept of hypothetical equivalents and expand the method to include hypothetical inequivalents. Visualization techniques, coupled with an indifference point analysis, are then used to understand the robustness of the solution obtained and determine the appropriate additional constraints necessary to identify a single robust optimal alternative. The same aircraft example is used to demonstrate the method of hypothetical equivalents and inequivalents.

Robust Multiattribute Decision Making Under Risk and Uncertainty in Engineering Design

In this article, the problem of choosing from a set of design alternatives based upon multiple, conflicting, and uncertain criteria is investigated. The problem of selection over multiple attributes becomes harder when risky alternatives exist. The overlap measure method developed in this article models two sources of uncertainties—imprecise or risky attribute values provided to the decision maker and inabilities of the decision-maker to specify an exact desirable attribute value. Effects of these uncertainties are mitigated using the overlap measure metric. A subroutine to this method, called the robust alternative selection method, ensures that the winning alternative is insensitive to changes in the relative importance of the different design attributes. The overlap measure method can be used to model and handle various sources of uncertainties and can be applied to any number of multiattribute decision-making methods. In this article, it is applied to the hypothetical equivalents and inequivalents method, which is a multiattribute selection method under certainty.

A constraint-based approach to feasibility assessment in preliminary design

In this paper, we present the development and application of a technical feasibility model used in preliminary design to determine whether a set of desired product specifications obtained from marketing is feasible in the engineering domain. This model is developed by integrating the capabilities of a multiobjective design problem, a multicriteria design optimization tool, a Pareto frontier gap analyzer, metamodeling methods, and use of the Pareto frontier as a constraint for feasibility assessment. Although the tools are independent of the domain, their application is illustrated using two examples: a simple three-objective mathematical problem and a five-objective passenger vehicle design problem. The feasibility of the desired product specifications is determined with respect to the problems Pareto frontier, which is considered to be the necessary constraint to satisfy.

A study of convergence and mapping in preliminary vehicle design

In this paper, we investigate the issue of convergence in multi-objective optimisation problems developed for vehicle analyses when using a Multi-Objective Genetic Algorithm (MOGA) to determine the set of Pareto optimal automobile configurations. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine new automobile design configurations for a given set of performance specifications. The advantage of this study is that the automobiles design information is obtained without having to repeat system analyses. The tools developed in this paper are applied both to a simple multi-objective optimisation problem to illustrate the methodology and to a preliminary vehicle design framework to develop a Technical Feasibility Model (TFM) for use in the early stages of automobile design.

A Study of Convergence and Mapping in Multiobjective Optimization Problems

In this paper, we investigate the issue of convergence in multiobjective optimization problems when using a MultiObjective Genetic Algorithm (MOGA) to determine the set of Pareto optimal solutions. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine design variable configurations for a given set of performance specifications. The advantage of this study is that the design variable information is obtained without having to repeat system analyses. The tools developed in this paper have been applied to develop a Technical Feasibility Model (TFM) used by General Motors as well as a simple multiobjective optimization problem in this paper. The multi-objective problem is primarily used to illustrate the developed methodology.

Feasibility Assessment in Preliminary Design Using Pareto Sets

In this paper, we present the development and application of a Technical Feasibility Model (TFM) used in preliminary design to determine whether or not a set of desired product specifications is technically feasible, and the optimality of those specifications with respect to the Pareto frontier. The TFM is developed by integrating the capabilities of a multidisciplinary design framework, a multi-objective design optimization tool, a Pareto set gap analyzer, metamodeling methods, and mathematical methods for feasibility assessment. This tool is then applied to a three objective example problem and to a five objective passenger vehicle design problem by analyzing benchmarking data from 78 late model sedans.

An Approach to Robust Decision Making In Multidisciplinary Selection Problems Under Uncertainty

In this paper, the problem of choosing from a set of design alternatives based upon multiple, conflicting and uncertain criteria is investigated. Multiple attributes can arise when different disciplines contribute to the design of a product or proc ess. The Overlap Measure Method developed in this paper models two sources of uncertainties – imprecise attribute values provided to the decision maker from the various disciplines and inabilities of the decision-maker to specify an exact desirable attribute level. Additionally, this method also provides the necessary steps to mitigate the effects of these sources of uncertainties using the Overlap Measure metric. The Overlap Measure Method can be applied to any theoretically sound multiattribute decision making method and in this paper is applied to the Hypothetical Equivalents and Inequivalents Method. The Overlap Measure Method also includes a sub -routine that ensures that the winning alternative is insensitive to changes in the relative importances of the different disciplines.

An Approach to Robust Multiattribute Concept Selection

In this paper, we investigate and extend a method of selecting among a set of concepts or alternatives using multiple, potentially conflicting criteria. This method, called the Hypothetical Equivalents and Inequivalents Method (HEIM), has been shown to avoid the many pitfalls of already existing methods for such problems, such as pair-wise comparison, ranking methods, rating methods, and weighted sum approaches. The existence of multiple optimal sets of attribute weights based on a set of stated preferences is investigated. Using simple visualization techniques, we show that there is a range of weights that satisfy the constraints of HEIM. Depending on the attribute weights used, multiple possible alternative winners could exist. The visualization techniques, coupled with an indifference point analysis, are then used to understand the robustness of the solution obtained and determine the appropriate additional constraints necessary to identify a single robust optimal alternative.Copyright

An Approach to Improved Decentralized Design: The Modified Approximation-Based Decentralized Design (MADD) Framework

The design of large scale complex engineering systems requires interaction and communication between multiple disciplines and decentralized subsystems. One common fundamental assumption in decentralized design is that the individual subsystems only exchange design variable information and do not share objective functions or gradients. This is because the decentralized subsystems can either not share this information due to geographical constraints or choose not to share it due to corporate secrecy issues. Game theory has been used to model the interactions between distributed design subsystems and predict convergence and equilibrium solutions. These game theoretic models assume that designers make perfectly rational decisions by selecting solutions from their Rational Reaction Set (RRS), resulting in a Nash Equilibrium solution. However, empirical studies reject the claim that decision makers always make rational choices and the concept of Bounded Rationality is used to explain such behavior. In this paper, a framework is proposed that uses the idea of bounded rationality in conjunction with set-based design, metamodeling and multiobjective optimization techniques to improve solutions for convergent decentralized design problems. Through the use of this framework, entitled Modified Approximation-based Decentralized Design (MADD) framework, convergent decentralized design problems converge to solutions that are superior to the Nash equilibrium. A two subsystem mathematical problem is used as case study and simulation techniques are used to study the impact of the framework parameters on the final solution. The discipline specific objective functions within the case study problem are unconstrained and continuous — however, the implementation of the MADD framework is not restricted to such problems.Copyright

THE USE OF REALLY SIMPLE SYNDICATION (RSS) FEEDS FOR IMPROVED INFORMATION COMMUNICATION IN DECENTRALIZED DECISION SYSTEMS

In today’s global product development environment, the design of large scale systems is completed by distributing design tasks to various subsystems that are located in different parts of the world. Though such a process reduces the product development time (due to concurrent completion of design tasks), there also exists the possibilities of errors due to information communication and handling among the distributed subsystems. Errors in information communication get amplified when there exist a large number of subsystems with high coupling between them. Causes for errors in design information communication include loss of design information emails, corruption of attachments that include product data, duplication of files at multiple locations, different software being used to complete the same design task, etc. All these factors contribute to delays in the product development process. In this paper, a new approach for design information communication that utilizes Really Simple Syndication (RSS) feeds is proposed. Most information providing websites provide their users with RSS feeds that include the latest available news. In this paper, the same process is incorporated for design information communication within decentralized design through the use of a web-based design environment. The design of a golf driver head by two subsystems is used as a case study.